Pfeiffer Presentation

Ernesto Carrella

September 9, 2016

The State of the Art

  • Random Utility Models
    • Statistically Efficient
    • Easily Generalizable
    • Policy-Brittle
  • Dynamic Programming
    • Strongly Rational
    • Computationally Expensive
    • Ad hoc

The One Agent Problem

  • Find the most profitable spot to fish
  • Constraints:
    • No biomass information
    • Environment changes over time
  • Subproblems:
    • How to explore
    • Explore-Exploit Tradeoff

One Agent sample run

Explore-Exploit

  • How to explore
    • Tow at a nearby cell from subjective best
    • Random Hill-Climbing
  • When to explore
    • Stochastically choose to explore next trip with probability \(p\)
    • Adjust \(p\) if exploration is often (un)succesful
  • Why not more nuanced bandit algorithms?

Explore-Exploit-Imitate

  • Other boats consume biomass
  • You can use other boats information
  • How to imitate?
  • With probability \(p\) explore, with probability \(i\) imitate a competitor otherwise exploit

Two Agents sample run

Many Agents

Oil Prices

Fish the Line (part 1)

Fish the Line (part 2)

Target Switching

Gear Selection

Simulating Quotas

  • TAC: Total Allowable Catch
  • ITQ: Individual Tradeable Quota

Single Species TAC vs ITQ (mileage)

Single Species TAC vs ITQ (catchability)

North-South world

Location choices

ITQ incentivates geography

Optimise Policies

Optimal TAC

Optimal ITQ

Well-Mixed World

OSMOSE

California

State of California

  • Biomass properly distributed
  • Gear and boats parametrized
  • Quotas correct, trading simplified
  • Not fitted to logbook data yet
  • Weather and fish movement missing

Winners and Losers

Winners and Losers (with policy)